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  "Title": "A Splicing Approach to the Inverse Problem of L0 Trend Filtering",
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  "Date": "2025-5-25",
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  "Description": "Trend filtering is a widely used nonparametric method for\nknot detection. This package provides an efficient solution for\nL0 trend filtering, avoiding the traditional methods of using\nLagrange duality or Alternating Direction Method of Multipliers\nalgorithms. It employ a splicing approach that minimizes\nL0-regularized sparse approximation by transforming the L0\ntrend filtering problem. The package excels in both efficiency\nand accuracy of trend estimation and changepoint detection in\nsegmented functions. References: Wen et al. (2020)\n<doi:10.18637/jss.v094.i04>; Zhu et al.\n(2020)<doi:10.1073/pnas.2014241117>; Wen et al. (2023)\n<doi:10.1287/ijoc.2021.0313>.",
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      "title": "A package for L0-regularized sparse approximation",
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        "L0TFinv-package"
      ]
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      "page": "coef.L0TFinvfix",
      "title": "Extract estimated trends",
      "topics": [
        "coef.L0TFinvfix",
        "coef.L0TFinvopt"
      ]
    },
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      "title": "Generate a difference matrix",
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        "DiffMat"
      ]
    },
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      "page": "L0TFinv.fix",
      "title": "The inverse L0 trend filtering with fixed change points",
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        "L0TFinv.fix"
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      "title": "The inverse L0 trend filtering with optimal change points",
      "topics": [
        "L0TFinv.opt"
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      "title": "Plot L0TFinvfix or L0TFinvopt object",
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        "plot.L0TFinvopt"
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        "print.L0TFinvopt"
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